code: def should_use_weight_file(val):
      return (
          val is not None
          and isinstance(val, (np.ndarray, np.generic))
          and val.size >= 10
          and val.dtype in ["float16", "float32", "uint8", "int8"]
      )
  
imports: import os
  import warnings
  from typing import Optional
  import numpy
  from coremltools import _OPSET, _SPECIFICATION_VERSION_IOS_15
  from coremltools import _logger
  from coremltools.converters.mil.backend.backend_helper import _get_probability_var_for_classifier
  from coremltools.converters.mil.backend.mil.helper import cast_to_framework_io_dtype, create_file_value, create_immediate_value, create_list_scalarvalue, create_scalar_value, types_to_proto
  from coremltools.converters.mil.backend.nn.load import _set_optional_inputs
  from coremltools.converters.mil.input_types import EnumeratedShapes, ImageType, RangeDim, TensorType
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Function, Program, mil_list, types
  from coremltools.converters.mil.mil.ops.registry import SSAOpRegistry
  from coremltools.converters.mil.mil.types.symbolic import any_symbolic, any_variadic, is_symbolic
  from coremltools.models.neural_network.flexible_shape_utils import NeuralNetworkImageSize, NeuralNetworkImageSizeRange, add_enumerated_image_sizes, add_multiarray_ndshape_enumeration, set_multiarray_ndshape_range, update_image_size_range
  from coremltools.models.utils import _WEIGHTS_FILE_NAME
  from coremltools.proto import FeatureTypes_pb2
  from coremltools.proto import MIL_pb2
  from coremltools.proto import Model_pb2
  from ..backend_helper import _get_colorspace_enum, _validate_image_input_output_shapes
label_desc: Use image node_id 1 for calling a global function with example usage: should_use_weight_file(val) and returns: unknown